Bayesian Estimation of the DINA Model With Gibbs Sampling

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Authors Steven Andrew Culpepper
Journal/Conference Name Journal of Educational and Behavioral Statistics
Paper Category
Paper Abstract A Bayesian model formulation of the deterministic inputs, noisy “and†gate (DINA) model is presented. Gibbs sampling is employed to simulate from the joint posterior distribution of item guessing and slipping parameters, subject attribute parameters, and latent class probabilities. The procedure extends concepts in BA©guin and Glas, Culpepper, and Sahu for estimating the guessing and slipping parameters in the three- and four-parameter normal-ogive models. The ability of the model to recover parameters is demonstrated in a simulation study. The technique is applied to a mental rotation test. The algorithm and vignettes are freely available to researchers as the “dina†R package.
Date of publication 2015
Code Programming Language R
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